Reporting on top of your data warehouse

Every row in your warehouse, in plain sight.

Rowlight turns the SQL your team already writes into dashboards people actually open. It reads each result, suggests the chart that fits, builds filters from your :keys, and gives everyone a Home with the dashboards they use most.

Read-only connections. Your data stays in your warehouse.

example data

1 · SQL

2 · Rows

3 · Chart

Works with the warehouse you already have

PostgreSQLMySQLBigQuerySnowflakeRedshiftClickHouseSQL Server

From a query to a dashboard in four steps

No semantic layer to model first and no drag-and-drop query builder to learn. If your team can write a SELECT, they can ship a dashboard the same afternoon.

Step 1

Connect a warehouse

Add a read-only connection per brand or business unit. Rowlight tests DNS, login and read-only access before you save.

Step 2

Write the SQL

Type :period or :currency and the filter appears. Unknown keys, typos in table names and missing date ranges are flagged as you type.

Step 3

Pick a chart by looking

Every chart family is drawn with your own rows. The best fit is marked, and hovering previews it full size.

Step 4

Share it

Drop the report on a dashboard, attach filters once for every card, and let people pin it to their Home.

Built around the SQL, not around a builder

Each part of Rowlight reads what you wrote and does the obvious next thing for you, while leaving the final say with you.

Charts that read your result

Rowlight profiles every result: row count, text, date and number columns, negative values. Six chart families are drawn with your data and the best fit is marked.

Barsbest fit
Trendno date column
Number42 rows

A sentence instead of a mapping form

Fields read like what the chart shows. Change any part from its dropdown; formats live next to the metric they belong to.

Show revenue for each channel · top 10 · high → low

Filters from your :keys

Attach a filter once to a whole dashboard. Rowlight maps it to every card whose SQL uses the key and tells you which cards would ignore it.

Period Last 30 daysRegion AllPlan Pro
Orders by dayuses :region
Revenue by channeluses :region
Refund rateno :region — ignores it

Dashboards that say what’s wrong

Thumbnails show each dashboard’s real layout. Failing and slow tiles, and dashboards nobody opens anymore, surface before someone asks.

1 tile failingPayments · data source login failed
1 slow tileRisk · KYC backlog takes 4.2 s
Not opened in 74 daysQA comparison

Errors in plain words

Rowlight checks columns against the schema and explains the problem with a one-click fix, instead of passing on a raw database error.

SUM(o.totl_amount)
orders has no column “totl_amount”. Did you mean total_amount?
Change to total_amount

A Home for every person

Rowlight suggests the dashboards each person opens most, so a new Home takes one click. Pins are personal and keep their order.

Sales dailyopened 23× this month
Paymentsopened 15×
Executiveopened 9×

Any industry with a warehouse

Rowlight doesn’t ship a data model for one industry. It works with the tables you have, so the same tool fits very different teams.

Online gaming

  • GGR by brand
  • RTP deviation by game
  • First-time deposits

E-commerce

  • Orders by channel
  • Basket size
  • Return rate by SKU

Fintech

  • Failed payments
  • KYC backlog
  • Chargebacks

SaaS

  • MRR and churn
  • Active accounts
  • Trial conversion

Logistics

  • On-time delivery
  • Late shipments
  • Cost per parcel

Bring your warehouse. We’ll bring the light.

A 30-minute demo on a copy of your own schema. You’ll leave with your first dashboard.